This paper makes a case for IT centralization on IBM i and explains the important role automated systems management technologies like Robot have to play in centralized IT environments.

Combining IBM Power Systems hardware and reliable iSeries job scheduler software is a powerful investment strategy for companies that not only improves their bottom line, but enhances customer experience.

At an application level with Vityl Capacity Management In this guide, John Miecielica of Metavante, provides a step-by-step example showing how he uses Vityl Capacity Management to analyze IT resource consumption at an application level. This key capability is especially important in today’s environments where multiple applications run on a server or multiple servers might be required to...

In this paper we present an introductory analysis of throughput scalability for update intensive workloads (such as measured by the TPC-C or TPC-W benchmarks) and how that scaling is limited by serialization effects in the software-hardware combination that comprises any platform.

In this online article Dr. Gunther digs down into the UNIX kernel to find out how load averages (the “LA Triplets”) are calculated and how appropriate they are as capacity planning metrics.

This is the second in a two part-series where we explore the use of UNIX load averages in performance analysis and capacity planning.

This is an unexpected Part 3 to the discussion about the UNIX load average metric answering the question of where the weight factor comes from.

This article is about delusions that arise from incorrect interpretation of load test measurements.

This paper discusses avoiding calculation results that are more precise than is justified by precision of corresponding measurement input data.

This article explains how correlated or bunched requests can impact capacity planning results.

This article explains how correlated or bunched requests can impact capacity planning results.

This is the final online article concerning the concept of application scalability. Here, you will learn how to determine value of the parameters that control scalability.